1. Field of the Invention
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for controlling data transfers from an origin compute node to a target compute node.
2. Description of Related Art
The development of the EDVAC computer system of 1948 is often cited as the beginning of the computer era. Since that time, computer systems have evolved into extremely complicated devices. Today's computers are much more sophisticated than early systems such as the EDVAC. Computer systems typically include a combination of hardware and software components, application programs, operating systems, processors, buses, memory, input/output devices, and so on. As advances in semiconductor processing and computer architecture push the performance of the computer higher and higher, more sophisticated computer software has evolved to take advantage of the higher performance of the hardware, resulting in computer systems today that are much more powerful than just a few years ago.
Parallel computing is an area of computer technology that has experienced advances. Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain results faster. Parallel computing is based on the fact that the process of solving a problem usually can be divided into smaller tasks, which may be carried out simultaneously with some coordination.
Parallel computers execute parallel algorithms. A parallel algorithm can be split up to be executed a piece at a time on many different processing devices, and then put back together again at the end to get a data processing result. Some algorithms are easy to divide up into pieces. Splitting up the job of checking all of the numbers from one to a hundred thousand to see which are primes could be done, for example, by assigning a subset of the numbers to each available processor, and then putting the list of positive results back together. In this specification, the multiple processing devices that execute the individual pieces of a parallel program are referred to as ‘compute nodes.’ A parallel computer is composed of compute nodes and other processing nodes as well, including, for example, input/output (‘I/O’) nodes, and service nodes.
Parallel algorithms are valuable because it is faster to perform some kinds of large computing tasks via a parallel algorithm than it is via a serial (non-parallel) algorithm, because of the way modern processors work. It is far more difficult to construct a computer with a single fast processor than one with many slow processors with the same throughput. There are also certain theoretical limits to the potential speed of serial processors. On the other hand, every parallel algorithm has a serial part and so parallel algorithms have a saturation point. After that point adding more processors does not yield any more throughput but only increases the overhead and cost.
Parallel algorithms are designed also to optimize one more resource the data communications requirements among the nodes of a parallel computer. There are two ways parallel processors communicate, shared memory or message passing. Shared memory processing needs additional locking for the data and imposes the overhead of additional processor and bus cycles and also serializes some portion of the algorithm.
Message passing processing uses high-speed data communications networks and message buffers, but this communication adds transfer overhead on the data communications networks as well as additional memory need for message buffers and latency in the data communications among nodes. Designs of parallel computers use specially designed data communications links so that the communication overhead will be small but it is the parallel algorithm that decides the volume of the traffic.
Many data communications network architectures are used for message passing among nodes in parallel computers. Compute nodes may be organized in a network as a ‘torus’ or ‘mesh,’ for example. Also, compute nodes may be organized in a network as a tree. A torus network connects the nodes in a three-dimensional mesh with wrap around links. Every node is connected to its six neighbors through this torus network, and each node is addressed by its x, y, z coordinate in the mesh. In a tree network, the nodes typically are connected into a binary tree: each node has a parent, and two children (although some nodes may only have zero children or one child, depending on the hardware configuration). In computers that use a torus and a tree network, the two networks typically are implemented independently of one another, with separate routing circuits, separate physical links, and separate message buffers.
A torus network lends itself to point to point operations, but a tree network typically is inefficient in point to point communication. A tree network, however, does provide high bandwidth and low latency for certain collective operations, message passing operations where all compute nodes participate simultaneously, such as, for example, an allgather.
When a passing messages between compute nodes, the compute node attempting to transmit a message is referred to as the ‘origin compute node,’ while the compute node intended as the recipient of the message is referred to as the ‘target compute node.’ To transfer data through a data communications network from an origin compute node to a target compute node, an application running on the origin compute node typically calls a ‘send’ messaging function of the origin compute node's messaging software and an application running on the target compute node typically calls a ‘receive’ messaging function of the target compute node's messaging software. When the origin compute node's application calls the ‘send’ messaging function, the origin compute node's messaging software transmits a request to send (‘RTS’) message to the messaging software of the target compute node that indicates that the origin compute node has data to transfer to the target compute node. The target compute node's messaging software then responds to the origin compute node with a clear to send (‘CTS’) message indicating that the target compute node is ready to receive the data from the transfer. Upon receiving the CTS message, the origin compute node's messaging software then transfers the data to the target compute node. Because the application's call to the ‘receive’ messaging function specifies the storage location for the data on the target compute node, if the application of the target compute node has not yet called the ‘receive’ messaging function by the time that the target node receives the data from the transfer, the target compute node's messaging software stores the transferred data in a temporary message buffer and waits for the target compute node's application to call the ‘receive’ function. The drawback to this current method of data transfer is that often the target compute node does not have excess memory storage to allow the messaging software to store the data from the transfer in a temporary buffer while waiting for the target compute node's application to call the ‘receive’ messaging function. As such, readers will appreciate that room for improvement exists in controlling data transfers from an origin compute node to a target compute node.